Agentic-SQL Taxonomy: A Survey of Autonomous and Interactive Text-to-SQL with LLMs

Agentic-SQL Taxonomy: A Survey of Autonomous and Interactive Text-to-SQL with LLMs | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 March 2026 V2 Latest version Share on Agentic-SQL Taxonomy: A Survey of Autonomous and Interactive Text-to-SQL with LLMs Authors : Yiyun Su 0009-0007-0854-7683 [email protected] , Huiying Zhu , Yu Tian , Changruo Zhao , Zujun Peng , Yuting Liu , Liang Fan , Baihua Li , and Luyan Zhang Authors Info & Affiliations https://doi.org/10.22541/au.177430005.57777158/v2 141 views 78 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Text-to-SQL systems have transitioned from simple machine translation models to complex reasoning frameworks as database schemas grow in scale and ambiguity. Despite the impressive capabilities of Large Language Models, one-shot generation often fails to produce correct SQL in real-world scenarios. This survey introduces the Agentic-SQL Taxonomy, an autonomy-based classification [21] that reevaluates existing methods through the lens of inference complexity. We categorize research into single-turn generation, iterative refinement, and multi-agent collaboration to highlight the shift toward interactive debugging and collective reasoning. We analyze how these sophisticated pipelines bridge the performance gap on challenging benchmarks. Current evaluations show that leading models can result in incorrect execution in nearly 40 % of cases when instructions are vague or incomplete. Our work identifies executionguided feedback and modular agent architectures as the primary drivers of future progress in building robust and reliable database interfaces. Supplementary Material File (text2sql (3).pdf) Download 1.43 MB Information & Authors Information Version history V1 Version 1 23 March 2026 V2 Version 2 24 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords agentic-sql taxonomy large language models text-to-sql Authors Affiliations Yiyun Su 0009-0007-0854-7683 [email protected] Rutgers University View all articles by this author Huiying Zhu ByteDance View all articles by this author Yu Tian Northeastern University View all articles by this author Changruo Zhao Rensselaer Polytechnic Institute View all articles by this author Zujun Peng Columbia University View all articles by this author Yuting Liu Harvard University View all articles by this author Liang Fan Loughborough University View all articles by this author Baihua Li Loughborough University View all articles by this author Luyan Zhang Northeastern University View all articles by this author Metrics & Citations Metrics Article Usage 141 views 78 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yiyun Su, Huiying Zhu, Yu Tian, et al. Agentic-SQL Taxonomy: A Survey of Autonomous and Interactive Text-to-SQL with LLMs. Authorea . 24 March 2026. 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